Artificial Intelligence Assisted Bone Age Assessment | NCT07121283

Studies
Artificial Intelligence Assisted Bone Age Assessment

NCT07121283

Sponsor

Cheng-Hsin General Hospital

Status

Enrolling by invitation

Last Update Posted

Apr 17, 2026Actual

Enrollment

8Estimated

Phase

Not Applicable

Conditions

Bone Age

Interventions

True AI
Fake AI

Countries

Taiwan

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Apr 2026

Overall Recruitment Status or Expanded Access Status

Enrolling by invitation

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Sep 1, 2025Actual

Primary Completion Date

Feb 18, 2026Actual

Completion Date

Aug 18, 2026Estimated

First Submitted Date

Jul 30, 2025

First Submission Date that Met QC Criteria

Aug 7, 2025

First Posted Date

Aug 13, 2025Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Apr 14, 2026

Last Update Posted Date

Apr 17, 2026Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief Summary

AI-Assisted Bone Age Assessment

Detailed Description

Artificial intelligence (AI) has gained great advancement in the application in clinical practice. However, this might introduce the automation bias, that the clinician over-rely on the incorrect advise from AI. The automation bias could have a great impact on the clinical decisions in an AI era. However, efforts to mitigate the automation bias tend to focus on upgrading AI performance and reducing bias in algorithms, which neglect the role of users. The aim of this study is to investigates the impact of the automation bias on the bone age assessment among radiologists with different seniority.

Design Module

Study Type

Interventional

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Not provided

Target Follow-Up Duration

Not provided

Phases

Not Applicable

Interventional Study Design

Allocation

Biospecimen

No data available

No data is available for this block.

Enrollment

8Estimated

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
True AI	Experimental		Device: True AI
Fake AI	Experimental		Device: Fake AI

Interventions

Name	Type	Description	Arm Group Labels	Other Names
True AI	Device	True output from bone age AI	True AI
Fake AI

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Difference of Skeletal Age Estimate	Mean absolute difference of bone age assessment between true AI-assisted and fake AI-assisted among radiologists	Five months

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Exams read by radiologists who interpret pediatric skeletal age exams and verbally consent to participate

Exclusion Criteria:

Exams containing more than one radiograph will not be included. No further exclusion criteria will be applied on the basis of image quality metrics or manufacturers

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

Not provided

Maximum Age

Not provided

Standard Ages

ChildAdultOlder Adult

Study Population

Not provided

Sampling Method

Not provided

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Name	Affiliation	Role
Yen-Huai Lin, Dr.	Cheng-Hsin General Hospital	Principal Investigator

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Cheng Hsin General Hospital		Taipei	Taiwan	112	Taiwan

IPD Sharing Statement Module

Plan to Share IPD

Description

Not provided

Types

Not provided

Time Frame

Not provided

Access Criteria

Not provided

URL

Not provided

Intervention Model Description

Six radiologists with different seniority (senior, intermediate, and junior) were recruited. The 200 standard films were evenly divided into two subsets: datasets A and B. A randomized crossover design with four periods (each with a 4-week duration) was arranged to minimize anticipation and carryover effects. Each radiologist who made an assessment with the aid of true AI or fake AI information could agree or disagree with the AI's predictions. The information of the fake AI was resulted from the randomization of the outputs from the true AI. Each radiologist was blinded to the existence of the fake AI.